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Updated: May 25, 2026

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A hidden Markov model-based technique for gait segmentation using a foot-mounted gyroscope.

Andrea Mannini1, Angelo Maria Sabatini

  • 1The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy. a.mannini@sssup.it

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
Summary

This study applies hidden Markov models (HMMs) to accurately identify key events in normal gait movement. The method precisely segments gait data, achieving over 95% accuracy in phase classification.

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Area of Science:

  • Biomechanics
  • Signal Processing
  • Machine Learning

Background:

  • Gait analysis is crucial for understanding human movement.
  • Accurate identification of gait events is challenging, especially with varying speeds and inclines.
  • Hidden Markov Models (HMMs) offer a probabilistic framework for sequential data analysis.

Purpose of the Study:

  • To apply hidden Markov models (HMMs) for time-locating specific events in normal gait.
  • To segment gait data collected under various walking conditions (speed, incline).
  • To evaluate the accuracy of HMMs in classifying gait phases and estimating key events.

Main Methods:

  • A four-state left-right HMM was trained using data from a mono-axial gyroscope.
  • Gyroscope signals were collected from the foot instep during treadmill walking at different speeds and inclines.

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  • A rule-based method was used for data annotation and event detection.

    • Main Results:

    • The HMM achieved sensitivity and specificity higher than 95% for gait phase classification.
    • The estimation accuracy for heel strike, flat foot, heel off, and toe off events was approximately 35 ms.
    • The model effectively segmented gait data across different walking conditions.

    Conclusions:

    • HMMs provide an effective tool for analyzing and segmenting gait data.
    • The developed method accurately identifies key gait events, demonstrating potential for clinical and research applications.
    • This approach enhances the precision of gait event detection in biomechanical studies.